Linearization and state estimation of unknown discrete-time nonlinear dynamic systems using recurrent neurofuzzy networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 29, Issue 6, 1999, Pages 802-817

  Gan, Qiang ^a   Harris, Chris J ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

B splines; Feedback linearization; Local linearization; Nonlinear system modeling; Recurrent neurofuzzy networks; State estimation

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; DISCRETE TIME CONTROL SYSTEMS; FEEDBACK; FUZZY SETS; GRAPH THEORY; INTERPOLATION; LEARNING ALGORITHMS; LINEARIZATION; RECURRENT NEURAL NETWORKS; STATE ESTIMATION;

ANALYSIS OF VARIANCE (ANOVA); RECURRENT NEUROFUZZY NETWORKS;

NONLINEAR CONTROL SYSTEMS;

EID: 0033280682     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.809034     Document Type: Article

References (39)

1. H. Tanizaki, Nonlinear Filters: Estimation and Applications. Berlin, Germany: Springer, 1996.
2. Q. Gan and C. J. Harris, "Fuzzy local linearization and local basis function expansion in nonlinear system modeling," IEEE Trans. Syst., Man, Cybern. B, vol. 29, pp. 559-565, Aug. 1999.
3. _, "Neurofuzzy state estimators using a modified ASMOD and Kalman filter algorithm," in Int. Conf. Computational Intelligence Modeling Control Automation, Vienna, Austria, Feb. 17-19, 1999.
4. R. Murray-Smith and T. A. Johansen, Multiple Model Approaches to Modeling and Control. London, U.K.: Taylor & Francis, 1997.
5. Z. Q. Wu and C. J. Harris, "A neurofuzzy network structure for modeling and state estimation of unknown nonlinear systems," Int. J. Syst. Sci., vol. 28, pp. 335-345, 1997.
6. H. Wang, M. Brown, and C. J. Harris, "Modeling and control of nonlinear, operating point dependent systems via associative memory networks," J. Dynam. Contr., vol. 6, pp. 199-218, 1996.
7. T. A. Johansen and B. A. Goss, "Constructing NARMAX models using ARMAX models," Int. J. Contr., vol. 58, pp. 1125-1153, 1993.
8. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665-685, 1993.
9. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116-132, 1985.
10. M. Vidyasagar, Nonlinear Systems Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1993.
11. J. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
12. A. Isidori, Nonlinear Control Systems. Berlin, Germany: Springer-Verlag, 1989.
13. C. Califano, S. Monaco, and D. Normand-Cyrot, "On the discrete-time normal form," IEEE Trans. Automat. Contr., vol. 43, pp. 1654-1658, Nov. 1998.
14. E. Armanda-Bricaire, U. Kotta, and C. H. Moog, "Linearization of discrete-time systems," SIAM J. Contr. Optim., vol. 34, pp. 1999-2023, 1996.
15. H. G. Lee and S. I. Marcus, "Approximate and local linearisability of nonlinear discrete-time systems," Int. J. Contr., vol. 44, no. 4, pp. 1103-1124, 1986.
16. S. Sastry and A. Isidori, "Adaptive control of linearizable systems," IEEE Trans. Automat. Contr., vol. 34, pp. 1123-1131, 1989.
17. M. French and E. Rogers, "Approximate models for adaptive feedback linearization," Int. J. Contr., vol. 68, no. 6, pp. 1305-1323, 1997.
18. C. C. Liu and F. C. Chen, "Adaptive control of nonlinear continuous time systems using neural networks-general relative degree and MIMO cases," Int. J. Contr., vol. 58, pp. 317-355, 1993.
19. F. C. Chen and C. C. Liu, "Adaptively controlling nonlinear continuous time systems using multilayer neural networks," IEEE Trans. Automat. Contr., vol. 39, pp. 1306-1310, June 1994.
20. F. C. Chen and H. K. Khalil, "Adaptive control of a class of nonlinear discrete-time systems using neural networks," IEEE Trans. Automat. Contr., vol. 40, pp. 791-801, May 1995.
21. A. Yesildirek and F. L. Lewis, "Feedback linearization using neural networks," Automatica, vol. 31, pp. 1659-1664, 1995.
22. S. Jagannathan and F. L. Lewis, "Multilayer discrete-time neural net controller with guaranteed performance," IEEE Trans. Neural Networks, vol. 7, no. 1, pp. 107-130, 1996.
23. _, "Feedback linearization in discrete-time using neural networks," in Proc. 12th IEEE Int. Symp. Intelligent Control, Istanbul, Turkey, 1997, pp. 181-186.
24. Y. H. Kim, F. L. Lewis, and C. T. Abdallah, "A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems," Automatica, vol. 33, no. 8, pp. 1539-1543, 1997.
25. R. Sanner and J. Slotine, "Gaussian networks for direct adaptive control," IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 837-863, 1992.
26. I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, "Systematic design of adaptive controllers for feedback linearizable systems," IEEE Trans. Automat. Contr., vol. 36, pp. 1241-1253, Nov. 1991.
27. M. French, "Adaptive control of functionally uncertain systems," Ph.D. dissertation, Southampton Univ., Southampton, U.K., 1998.
28. R. Marino and P. Tomei, "Global adaptive output-feedback control of nonlinear systems, Part I: Linear parameterization," IEEE Trans. Automat. Contr., vol. 38, pp. 17-32, Jan. 1993.
29. _, "Global adaptive output-feedback control of nonlinear systems, Part II: Nonlinear parameterization," IEEE Trans. Automat. Contr., vol. 38, pp. 33-48, Jan. 1993.
30. _, "Global adaptive observers for nonlinear systems via filtered transformations," IEEE Trans. Automat. Contr., vol. 37, pp. 1239-1245, Aug. 1992.
31. R. Marino and P. Tomei, "Adaptive observers with arbitrary exponential rate of convergence for nonlinear systems," IEEE Trans. Automat. Contr., vol. 40, pp. 1300-1304, July 1995.
32. M. Krstic and P. V. Kokotovic, "Adaptive nonlinear output-feedback schemes with Marino-Tomei controller," IEEE Trans. Automat. Contr., vol. 41, pp. 274-280, Feb. 1996.
33. L. Jin, P. N. Nikiforuk, and M. M. Gupta, "Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks," IEEE Trans. Automat. Contr., vol. 40, no. 7, pp. 1266-1270, 1995.
34. E. Sontag, "Recurrent neural networks: Some systems-theoretic aspects," in Dealing with Complexity: A Neural Network Approach, M. Karny, K. Warwick, and V. Kurkova, Eds. Berlin, Germany: Springer-Verlag, 1997, pp. 1-12.
35. F. J. Pineda, "Generalization of back-propagation to recurrent neural networks," Phys. Rev. Lett., vol. 59, no. 19, pp. 2229-2232, 1987.
36. B. A. Pearlmutter, "Gradient calculations for dynamic recurrent neural networks: A survey," IEEE Trans. Neural Networks, vol. 6, no. 5, pp. 1212-1228, 1995.
37. G. Hardier, "Recurrent neural networks for ship modeling and control," in 11th Ship Control Systems Symp., 1997, vol. 1, pp. 39-61.
38. G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ: Prentice-Hall, 1984.
39. C. K. Chui and G. Chen, Kalman Filtering: With Real-Time Applications. Berlin, Germany: Springer-Verlag, 1987.